Speaker
Dr
Attila Krasznahorkay
(New York University)
Description
In a typical offline data analysis in high-energy-physics a large number of
collision events are studied. For each event the reconstruction software of
the experiments stores a large number of measured event properties in
sometimes complex data objects and formats. Usually this huge amount
of initial data is reduced in several analysis steps, selecting a subset of
interesting events and observables. In addition, the same selection is applied
to simulated MC events and the final results are compared to the data. A
fast processing of the events is mandatory for an efficient analysis.
In this paper we introduce the SFrame package, a ROOT-based analysis
framework, that is widely used in the context of ATLAS data analyses.
It features (i) consecutive data reduction in multiple user-defined analysis
cycles performing a selection of interesting events and observables, making
it easy to calculate and store new derived event variables; (ii) a
user-friendly combination of data and MC events using weighting techniques;
and in particular (iii) a high-speed processing of the events. We study the
timing performance of SFrame and find a highly superior performance compared to
other analysis frameworks.
More information can be found at: http://sourceforge.net/projects/sframe/
Summary
The structure and performance of SFrame is presented, which is a light-weight analysis-framework built around ROOT.
Primary authors
Dr
Attila Krasznahorkay
(New York University)
Dr
David Berge
(CERN)
Dr
Johannes Haller
(Hamburg Univeristy)
